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       <h1 id="tutorials-torchtrtc--page-root">
        torchtrtc
        <a class="headerlink" href="#tutorials-torchtrtc--page-root" title="Permalink to this headline">
         ¶
        </a>
       </h1>
       <p>
        <code class="docutils literal notranslate">
         <span class="pre">
          torchtrtc
         </span>
        </code>
        is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
a deployment pipeline. All basic features of the compiler are supported including post training
quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
output two formats, either a TorchScript program with the TensorRT engine embedded or
the TensorRT engine itself as a PLAN file.
       </p>
       <p>
        All that is required to run the program after compilation is for C++ linking against
        <code class="docutils literal notranslate">
         <span class="pre">
          libtorchtrt.so
         </span>
        </code>
        or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
to standard TorchScript. Load with
        <code class="docutils literal notranslate">
         <span class="pre">
          torch.jit.load()
         </span>
        </code>
        and run like you would run any other module.
       </p>
       <div class="highlight-txt notranslate">
        <div class="highlight">
         <pre><span></span>torchtrtc [input_file_path] [output_file_path]
  [input_specs...] {OPTIONS}

  torchtrtc is a compiler for TorchScript, it will compile and optimize
  TorchScript programs to run on NVIDIA GPUs using TensorRT

OPTIONS:

    -h, --help                        Display this help menu
    Verbiosity of the compiler
      -v, --verbose                     Dumps debugging information about the
                                        compilation process onto the console
      -w, --warnings                    Disables warnings generated during
                                        compilation onto the console (warnings
                                        are on by default)
      --i, --info                       Dumps info messages generated during
                                        compilation onto the console
    --build-debuggable-engine         Creates a debuggable engine
    --allow-gpu-fallback              (Only used when targeting DLA
                                      (device-type)) Lets engine run layers on
                                      GPU if they are not supported on DLA
    --require-full-compilation        Require that the model should be fully
                                      compiled to TensorRT or throw an error
    --check-method-support=[method_name]
                                      Check the support for end to end
                                      compilation of a specified method in the
                                      TorchScript module
    --disable-tf32                    Prevent Float32 layers from using the
                                      TF32 data format
    --sparse-weights                  Enable sparsity for weights of conv and
                                      FC layers
    -p[precision...],
    --enable-precision=[precision...] (Repeatable) Enabling an operating
                                      precision for kernels to use when
                                      building the engine (Int8 requires a
                                      calibration-cache argument) [ float |
                                      float32 | f32 | fp32 | half | float16 |
                                      f16 | fp16 | int8 | i8 | char ]
                                      (default: float)
    -d[type], --device-type=[type]    The type of device the engine should be
                                      built for [ gpu | dla ] (default: gpu)
    --gpu-id=[gpu_id]                 GPU id if running on multi-GPU platform
                                      (defaults to 0)
    --dla-core=[dla_core]             DLACore id if running on available DLA
                                      (defaults to 0)
    --engine-capability=[capability]  The type of device the engine should be
                                      built for [ standard | safety |
                                      dla_standalone ]
    --calibration-cache-file=[file_path]
                                      Path to calibration cache file to use
                                      for post training quantization
    --teo=[op_name...],
    --torch-executed-op=[op_name...]  (Repeatable) Operator in the graph that
                                      should always be run in PyTorch for
                                      execution (partial compilation must be
                                      enabled)
    --tem=[module_name...],
    --torch-executed-mod=[module_name...]
                                      (Repeatable) Module that should always
                                      be run in Pytorch for execution (partial
                                      compilation must be enabled)
    --mbs=[num_ops],
    --min-block-size=[num_ops]        Minimum number of contiguous TensorRT
                                      supported ops to compile a subgraph to
                                      TensorRT
    --embed-engine                    Whether to treat input file as a
                                      serialized TensorRT engine and embed it
                                      into a TorchScript module (device spec
                                      must be provided)
    --num-min-timing-iter=[num_iters] Number of minimization timing iterations
                                      used to select kernels
    --num-avg-timing-iters=[num_iters]
                                      Number of averaging timing iterations
                                      used to select kernels
    --workspace-size=[workspace_size] Maximum size of workspace given to
                                      TensorRT
    -t[threshold],
    --threshold=[threshold]           Maximum acceptable numerical deviation
                                      from standard torchscript output
                                      (default 2e-5)
    --no-threshold-check              Skip checking threshold compliance
    --truncate-long-double,
    --truncate, --truncate-64bit      Truncate weights that are provided in
                                      64bit to 32bit (Long, Double to Int,
                                      Float)
    --save-engine                     Instead of compiling a full a
                                      TorchScript program, save the created
                                      engine to the path specified as the
                                      output path
    input_file_path                   Path to input TorchScript file
    output_file_path                  Path for compiled TorchScript (or
                                      TensorRT engine) file
    input_specs...                    Specs for inputs to engine, can either
                                      be a single size or a range defined by
                                      Min, Optimal, Max sizes, e.g.
                                      "(N,..,C,H,W)"
                                      "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
                                      Data Type and format can be specified by
                                      adding an "@" followed by dtype and "%"
                                      followed by format to the end of the
                                      shape spec. e.g. "(3, 3, 32,
                                      32)@f16%NHWC"
    "--" can be used to terminate flag options and force all following
    arguments to be treated as positional options
</pre>
        </div>
       </div>
       <p>
        e.g.
       </p>
       <div class="highlight-shell notranslate">
        <div class="highlight">
         <pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts <span class="s2">"[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous"</span> -p f16
</pre>
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